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• W4387492725 startingPage "121963" @default.
• W4387492725 abstract "The continuous casting process, used in the manufacture of steel plates, is currently the most economical and efficient way of production. Although continuous casting is a well-established process, many associated issues remain to be resolved, including obstructions that occur in the Submerged Entry Nozzle (SEN) that controls the flow of steel between the tundish and the mold. Clogging on SEN not only impairs the quality of the product but also results in lower process yield, resulting in losses. In this context, we evaluated three methodologies based on deep neural networks to detect the occurrences of clogging in a steel industry from historical data of process variables. As a baseline, models based on traditional artificial neural networks were used. The overall performance of the classifiers developed here showed very promising results in real data with unbalanced classes. In particular, the method employing spatiotemporal analysis obtained a remarkably superior performance with respect to the other methods, achieving a Recall and a Precision of almost 99% and 98%, respectively. The code is available for public access through the link: https://github.com/apaulinhamd/Deep-Neural-Networks-For-Clogging-Detection." @default.
• W4387492725 created "2023-10-11" @default.
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• W4387492725 date "2024-03-01" @default.
• W4387492725 modified "2023-10-18" @default.
• W4387492725 title "Use of deep neural networks for clogging detection in the Submerged Entry Nozzle of the continuous casting" @default.
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• W4387492725 doi "https://doi.org/10.1016/j.eswa.2023.121963" @default.
• W4387492725 hasPublicationYear "2024" @default.
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